Summary of Explainability For Machine Learning Models: From Data Adaptability to User Perception, by Julien Delaunay
Explainability for Machine Learning Models: From Data Adaptability to User Perception
by julien Delaunay
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This thesis tackles the challenge of creating local explanations for deployed machine learning models, seeking optimal conditions for producing meaningful explanations that balance data and user requirements. The researchers aim to develop methods generating explanations for any model while ensuring these explanations remain faithful to the underlying model and comprehensible to users. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Local explanations are crucial for building trust in AI systems. This thesis aims to make machine learning more transparent by developing methods for generating local explanations for deployed models. By balancing data and user requirements, the researchers aim to produce explanations that are both accurate and easy to understand. |
Keywords
* Artificial intelligence * Machine learning
